Single-inclusive production of large-pT charged particles in hadronic collisions at TeV energies and perturbative QCD predictions

The single inclusive spectrum of charged particles with transverse momenta pT=3-150 GeV/c measured at midrapidity by the CDF experiment in proton-antiproton (p-pbar) collisions at sqrt(s)=1.96 TeV is compared to next-to-leading order (NLO) perturbative QCD calculations using the most recent parametrizations of the parton distributions and parton-to-hadron fragmentation functions. Above pT~20 GeV/c, there is a very sizeable disagreement of the Tevatron data compared to the NLO predictions and to xT-scaling expectations, suggesting a problem in the experimental data. We also present the predictions for the pT-differential charged hadron spectra and the associated theoretical uncertainties for proton-proton (p-p) collisions at LHC energies (sqrt(s)=0.9-14 TeV). Two procedures to estimate the charged hadron spectra at LHC heavy-ion collision energies (sqrt(s)=2.76,5.5 TeV) from p-p measurements are suggested.Comment: 23 pages, 9 figures. A few text additions. Accepted for publication in JHE

Character-Aware Neural Language Models

We describe a simple neural language model that relies only on character-level inputs. Predictions are still made at the word-level. Our model employs a convolutional neural network (CNN) and a highway network over characters, whose output is given to a long short-term memory (LSTM) recurrent neural network language model (RNN-LM). On the English Penn Treebank the model is on par with the existing state-of-the-art despite having 60% fewer parameters. On languages with rich morphology (Arabic, Czech, French, German, Spanish, Russian), the model outperforms word-level/morpheme-level LSTM baselines, again with fewer parameters. The results suggest that on many languages, character inputs are sufficient for language modeling. Analysis of word representations obtained from the character composition part of the model reveals that the model is able to encode, from characters only, both semantic and orthographic information.Comment: AAAI 201

Mapless Online Detection of Dynamic Objects in 3D Lidar

This paper presents a model-free, setting-independent method for online detection of dynamic objects in 3D lidar data. We explicitly compensate for the moving-while-scanning operation (motion distortion) of present-day 3D spinning lidar sensors. Our detection method uses a motion-compensated freespace querying algorithm and classifies between dynamic (currently moving) and static (currently stationary) labels at the point level. For a quantitative analysis, we establish a benchmark with motion-distorted lidar data using CARLA, an open-source simulator for autonomous driving research. We also provide a qualitative analysis with real data using a Velodyne HDL-64E in driving scenarios. Compared to existing 3D lidar methods that are model-free, our method is unique because of its setting independence and compensation for pointcloud motion distortion.Comment: 7 pages, 8 figure

Learning a Bias Correction for Lidar-only Motion Estimation

This paper presents a novel technique to correct for bias in a classical estimator using a learning approach. We apply a learned bias correction to a lidar-only motion estimation pipeline. Our technique trains a Gaussian process (GP) regression model using data with ground truth. The inputs to the model are high-level features derived from the geometry of the point-clouds, and the outputs are the predicted biases between poses computed by the estimator and the ground truth. The predicted biases are applied as a correction to the poses computed by the estimator. Our technique is evaluated on over 50km of lidar data, which includes the KITTI odometry benchmark and lidar datasets collected around the University of Toronto campus. After applying the learned bias correction, we obtained significant improvements to lidar odometry in all datasets tested. We achieved around 10% reduction in errors on all datasets from an already accurate lidar odometry algorithm, at the expense of only less than 1% increase in computational cost at run-time.Comment: 15th Conference on Computer and Robot Vision (CRV 2018

Sustainability of Concrete as A Civil Engineering Material

With increasing concern about the environment, energy consumption, climate change, and depletion of natural resources, the importance of sustainability has become mainstream among engineering and scientific communities. Concrete infrastructure is superbly durable and comes with a myriad of benefits. Yet, the production of concrete is energy intensive and represents a substantial portion of air pollution. Largely due to cement manufacturing, concrete represents 7% of greenhouse gas emissions globally and 1% in the United States. Focusing on sector-specific emissions in the United States., this paper outlines the environmental concerns of concrete production and discusses the forefront of research in reducing these effects including innovations in cement manufacturing, alternative clinker technologies, and carbon capture use and storage. Also discussed are various approaches and efforts in concrete recycling and incorporation of industrial wastes and supplementary cementitious materials into concrete. Finally, this study reviews the role of civil engineering design at various scales in the sustainability of concrete infrastructure